Data Warehouse, Data Lake, Database: Which is the Best Choice?


Collecting data is vital for everyone who works with it. Many organizations are trying to make the most of valuable data.

Data is often collected using traditional databases. These databases are small and store structured data that is processed slowly.

Today, data lakes or data warehouses are often used to store large amounts of data. These two concepts are often compared and discussed, although, in reality, they are very different. Both in terms of people and purpose and duration of structure and processing.

This is what we want to talk about in this article. History, differences, possible applications, and technical examples.

What is Data Warehouse, Data Lake, and Database?

First of all, let’s get to know the different types of data storage solutions.

What Is a Database?

A database is a place where well-organized data (also known as structured data) is stored from a single source. It can search, store, and discover its contents. A database can be a computer that organizes data into a database.

This is the simplest way to create a data storage system. SQL is used to query data. It is often used to produce reports, including financial reports. Concise data analysis is needed to automate business processes and validate information.

Examples Of Database Technologies


PostgreSQL is an open-source database with many advanced features. It is also known as a free version of Oracle.


MySQL is one of the most widely used databases; fast, easy to use, and open source.

Oracle, Microsoft SQL Server, and MongoDB are also available in different versions. Regarding data storage formats, PostgreSQL works well with SQL, and MongoDB works well with NoSQL databases, including Oracle, MySQL, and Microsoft SQL Server.

After databases, we will look at data warehouses.

Data Warehouse: What Is It?

A data warehouse is a large area used to store data. It stores well-structured data from different sources (complex data warehouses can also store semi-structured data). Business analysts can collect data and generate reports to help management make business decisions. This can be called the basis of data analytics.

Let’s look at some examples of technology solutions offering data warehouses to understand them and not be surprised when we hear about them.

Examples Of Data Warehouse Technologies

Amazon Redshift

Amazon Redshift is one of the most widely used data warehouse solutions. It provides users with structured data and integrates well with AWS configurations and data lakes. Companies like McDonald’s, Nasdaq, and Pfizer use Redshift to store semi-structured data.


Another well-known data warehouse is Snowflake Elastic Data Warehouse, which can transform companies into data-driven enterprises. If you use it, you have to pay for it. Companies using Snowflake Elastic include Informatica and Tableau.

Planning is critical when creating a data warehouse. Data warehouses are used to store data in an organized way and can store both past and current data. The capacity of a data warehouse is limited to well-structured data.

A data warehouse helps to overcome this limitation.

Data Lake: What Is It?

A data lake is a large container where you can store data from different sources without transforming it. Simply put, it allows you to store raw data. Semi-structured data differs from data with a clear structure, semi-structured data, and unstructured data without a defined structure.

The structure of data can be modified. Data experts or data analysts create models. Data can be analyzed in real-time, but extracting data from the data warehouse and using it to produce reports or conclusions for business decisions is more difficult because the data must be in a readable format.

In addition, data lakes offer greater flexibility and lower storage costs than data warehouses. However, they cannot be replaced. It all depends on the organization’s objectives and goals. 

Examples of Data Lake Technologies

Azure Data Lake on the Microsoft Azure Platform

The main goal of Azure Data Lake is to store all data in one place. It maintains a robust data protection architecture. The retrievability of data is limited by its location. Marks & Spencer, Smiths Group, and Rockwell Automation are some companies using this solution.

Amazon S3

When it comes to providing industrial services for big data, Amazon S3 is the data provider. Companies working on data efficiency and security cite Airbnb and Netflix as examples.

How Do You Choose Between a Data Warehouse, A Data Lake, And A Database?

It is well known that there are three types of data storage systems. But you still need to know which one to use. Let’s look at the options so that everyone knows what exists and which data storage solutions can be used to meet the needs of the whole organization and explore the model below.

Data Architecture

  • Think of the many sources from which we draw knowledge. How are they organized? How often and how fast does data flow?
  • Data from different sources, structured and unstructured, can be stored in a repository, such as a data warehouse.
  • Only well-structured data from different sources can be stored in a data warehouse, but nowadays, data warehouses also store semi-structured data.
  • A data warehouse can only store well-structured data from one source.

Data Processing Requirements

  • The modeling process should be easy to understand. It should then be aligned with the organization’s data management plan.
  • Data lakes are more flexible in terms of storage. This is because data lakes have a schema that allows raw data and metadata to be stored in the same file that receives the structured data for analysis.
  • Before the data can be used in a database or data warehouse, the original data must be transformed into structured data – a process called ETL (Extract, Transform, Load).

Budget Constraints and Data Storage

  • Businesses can undoubtedly benefit from large amounts of data, but traffic and the amount of data are increasing rapidly. At the same time, data storage costs are rising.
  • Data lakes are the cheapest because various forms of raw data can be used to store data.
  • Databases are designed to store data in mobile devices and applications. They can be scaled up or down depending on the data needed. Simplified cost management.
  • Data warehouses use space to process data. They store data for later analysis. This entails significant costs depending on the process.

Justification and Users

  • If we want to use a data warehouse to capture business information and present it to management, then a data warehouse will meet our needs. However, the cost of data warehouses is increasing.
  • Data geeks prefer data lakes because they allow building machine learning (ML) and artificial intelligence (AI) systems on structured and unstructured data.
  • Generally, data lakes are used to store continuous transactional data, which is the right choice for SQL-savvy users. The primary purpose of a data lake is to store data in a database.

Data Ecosystems and Technologies

  • The emergence of Hadoop technology, which can process large amounts of data and run on computer clusters, has increased the popularity of data lakes. In addition, unstructured data can be collected in real-time from an organization’s internal systems.
  • However, the complexity of data updating and user access must be considered. If an organization’s data management system changes, this may affect data sources or data structure.
  • Changing databases and data warehouses are more costly as they may have to be designed and built from scratch.

Overview of the Methods Used for Data Collection

Now that you know the three main types of data management, you can assume that if you have any data, you use a database, data warehouse, or data lake for some reason.

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